Modern Analytics in the Harvard Business Review

Winter 2013 – 2014

Harvard-Business-School-logo1Modern Analytics was featured in the Harvard Business Review’s December 2013 edition in an article by Thomas H. Davenport entitled “Big Data at Work: Dispelling the Myths, Uncovering the Opportunities.”

In the article, Davenport discusses Analytics 3.0, a new wave of business analytics that enables companies to apply data science to optimize their operations, products, and services at levels previously unforeseen. He introduces emerging technologies and what they mean for the changing analytics landscape. Davenport discusses how Modern Analytics uses Model Factory, data assembly, automation, and analytics to change the way enterprises interact with and use data science.

From the article’s introduction:

Some of us now perceive another shift, fundamental and far-reaching enough that we can fairly call it Analytics 3.0. Briefly, it is a new resolve to apply powerful data-gathering and analysis methods not just to a company’s operations but also to its offerings — to embed data smartness into the products and services customers buy.

I’ll develop this argument in what follows, making the case that just as the early applications of big data marked a major break from the 1.0 past, the current innovations of a few industry leaders are evidence that a new era is dawning. When a new way of thinking about and applying a strength begins to take hold, managers are challenged to respond in many ways. Change comes fast to every part of a business’s world. New players emerge, competitive positions shift, novel technologies must be mastered, and talent gravitates toward the most exciting new work.

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Analytics 3.0

Those of us who have spent years studying “data smart” companies believe we’ve already lived through two eras in the use of analytics. We might call them BBD and ABD—before big data and after big data. Or, to use a naming convention matched to the topic, we might say that Analytics 1.0 was followed by Analytics 2.0. Generally speaking, 2.0 releases don’t just add some bells and whistles or make minor performance tweaks. In contrast to, say, a 1.1 version, a 2.0 product is a more substantial overhaul based on new priorities and technical possibilities. When large numbers of companies began capitalizing on vast new sources of unstructured, fast-moving information—big data—that was surely the case.

Some of us now perceive another shift, fundamental and far-reaching enough that we can fairly call it Analytics 3.0. Briefly, it is a new resolve to apply powerful data-gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy.

I’ll develop this argument in what follows, making the case that just as the early applications of big data marked a major break from the 1.0 past, the current innovations of a few industry leaders are evidence that a new era is dawning. When a new way of thinking about and applying a strength begins to take hold, managers are challenged to respond in many ways. Change comes fast to every part of a business’s world. New players emerge, competitive positions shift, novel technologies must be mastered, and talent gravitates toward the most exciting new work.

Managers will see all these things in the coming months and years. The ones who respond most effectively will be those who have connected the dots and recognized that competing on analytics is being rethought on a large scale. Indeed, the first companies to perceive the general direction of change—those with a sneak peek at Analytics 3.0—will be best positioned to drive that change.

The Evolution of Analytics

My purpose here is not to make abstract observations about the unfolding history of analytics. Still, it is useful to look back at the last big shift and the context in which it occurred. The use of data to make decisions is, of course, not a new idea; it is as old as decision making itself. But the field of business analytics was born in the mid-1950s, with the advent of tools that could produce and capture a larger quantity of information and discern patterns in it far more quickly than the unassisted human mind ever could.

Analytics 1.0—the era of “business intelligence.”

What we are here calling Analytics 1.0 was a time of real progress in gaining an objective, deep understanding of important business phenomena and giving managers the fact-based comprehension to go beyond intuition when making decisions. For the first time, data about production processes, sales, customer interactions, and more were recorded, aggregated, and analyzed.

New computing technologies were key. Information systems were at first custom-built by companies whose large scale justified the investment; later, they were commercialized by outside vendors in more-generic forms. This was the era of the enterprise data warehouse, used to capture information, and of business intelligence software, used to query and report it.

New competencies were required as well, beginning with the ability to manage data. Data sets were small enough in volume and static enough in velocity to be segregated in warehouses for analysis. However, readying a data set for inclusion in a warehouse was difficult. Analysts spent much of their time preparing data for analysis and relatively little time on the analysis itself.